Evolutionary algorithms for geographic load balancing using a distributed antenna system

ABSTRACT

Methods and apparatuses are presented for balancing non-uniformly distributed network traffic in a wireless communications system having a plurality of digital remote units (DRUs). In some embodiments, a method comprises partitioning the plurality of DRUs into a plurality of DRU sectors, and dynamically repartitioning the plurality of DRU sectors depending on traffic conditions in at least one of the DRU sectors, such that the repartitioning satisfies at least one of a soft capacity constraint or a hard capacity constraint. The dynamic repartitioning may be based on at least one optimization algorithm.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/600,530, filed on Feb. 17, 2012, and titled “EvolutionaryAlgorithms for Geographic Load Balancing Using a Distributed AntennaSystem,” the disclosure of which is hereby incorporated by reference inits entirety for all purposes.

BACKGROUND OF THE INVENTION

Distributed antenna systems (DAS) have been widely implemented instate-of-the art cellular communication systems to enhance wirelessnetwork capabilities. DAS has discernible advantages over conventionalcommunication systems, due to its architectural design compared totraditional wireless network installations. Recent studies haveidentified other potential advantages for using DAS. However, most ofthe recent work on DAS does not focus on load balancing techniques foroptimizing such performance. Therefore, there is a need in the art forimproved methods and systems related to load balancing in DAS.

SUMMARY OF THE INVENTION

With the increase of cellular users, traffic hot spots and unbalancedtraffic distributions are common in wireless networks. Embodiments ofthe present invention relate to a load-balancing system for mobilenetworks that optimizes cellular performance according to the trafficgeographic distribution in order to provide a high quality of service(QoS). In some embodiments, a Virtualized Distributed Antenna System(VDAS) fed by a multi-sector Base Transceiver Station (BTS) has theability to distribute the cellular capacity over a given geographicarea. The VDAS can focus a portion of the BTS capacity around traffic“hot spots” and redistribute the remaining BTS capacity over the othercells. To enable load balancing among Distributed Antennas (DAs),embodiments of the invention may dynamically allocate DAs to the BTSsectors, depending on the time-varying traffic. VDAS sectorization isformulated as an integer linear constrained optimization problem in someembodiments, which minimizes the blocked calls and handoffs as twoimportant key performance indicators (KPIs) in the network. In someembodiments, a VDAS sectorization technique considers connected andcompact sectors to minimize the number of handoffs, while satisfying thecapacity demand at each sector. Various embodiments include at least oneof two evolutionary algorithms to solve the problem: Genetic Algorithm(GA) and Estimation Distribution Algorithm (EDA). Results are presentedthat evaluate the system performance for different traffic scenarios.Results demonstrate that the two algorithms attain excellent KPIs forsmall-scale networks.

Embodiments of the present invention include methods and apparatuses foroptimizing traffic in VDAS wireless communications systems.Sectorization of DRUs is examined to balance the traffic in the VDAS LTEsystem. Proper sectorization is considered to effectively use theresources (licenses) in each sector that satisfies the soft and hardcapacity. Connected and compact sectors are proposed to reduce handoffsand interferences. The DRU sectorization is formulated as an integerlinear programming problem which minimizes blocked and handoff calls insome embodiments.

Embodiments including EDA and GA algorithms are disclosed to solve thesectorization problem and the solutions are compared with each other andoptimal or lower bound solutions. Using three test problems, the EDAalgorithm produced decidedly better and faster results than the GAalgorithm and achieved optimality on the problems whose size allowedenumeration. Outstanding performance is illustrated by the EDA and GAalgorithms, for small networks (19_DRU). For large networks, EDAdemonstrates excellent quality in a reasonable time.

According to an embodiment of the present invention, a method forbalancing non-uniformly distributed network traffic in a wirelesscommunications system having a plurality of digital remote units (DRUs)is provided. The method includes partitioning the plurality of DRUs intoa plurality of DRU sectors dynamically repartitioning the plurality ofDRU sectors depending on traffic conditions in at least one of the DRUsectors, such that the repartitioning satisfies at least one of a softcapacity constraint or a hard capacity constraint. The dynamicrepartitioning is based on at least one optimization algorithm. As anexample, the at least one optimization algorithm can include at leastone evolutionary algorithm such as either a Genetic Algorithm (GA) or anEstimation Distribution Algorithm (EDA).

In some embodiments, the at least one optimization algorithm includes atleast one linear programming model including at least one of thefollowing constraints: minimizing blocked calls, minimizing callhandoffs, or maximizing a compactness index of each of the DRU sectors.Each of the DRU sectors can include connected DRUs and each of the DRUsectors can be substantially compact. As an example, the soft capacityconstraint can include a maximum number of users in order to maintain anacceptable signal to noise ratio (SNR). As another example, the hardcapacity constraint can include a total number of licenses/sourcesassigned to a virtual base station (VBS).

According to another embodiment of the present invention, a method forbalancing non-uniformly distributed network traffic in a wirelesscommunications system having a plurality of digital remote units (DRUs)is provided. The method includes a) providing a digital access unit(DAU) associated with a plurality of sectors of a base station and b)providing a plurality of DRUs associated with the DAU. The method alsoincludes c) partitioning the plurality of DRUs into a plurality of DRUsectors and d) measuring a number of blocked calls associated with theplurality of DRU sectors. The method further includes e) determiningthat the number of blocked calls is greater than a predeterminedthreshold and f) iterating using at least elements (c) through (e).

According to a specific embodiment of the present invention, a methodfor balancing non-uniformly distributed network traffic in a DistributedAntenna System (DAS) is provided. The method includes a) providing adigital access unit (DAU) associated with a plurality of sectors of abase station and b) providing a plurality of Digital Remote Units (DRUs)associated with the DAU. The method also includes c) partitioning theplurality of DRUs into a plurality of DRU sectors and d) measuring atleast one metric for traffic conditions associated with the plurality ofDRU sectors. The method further includes e) comparing the at least onemetric to a predetermined threshold, f) determining that iteration ofpartitioning is warranted, and g) iterating on at least elements (c)through (e).

According to another specific embodiment of the present invention, aDistributed Antenna System (DAS) is provided. The DAS includes one ormore Digital Access Units (DAUs) and a plurality of Digital Remote Units(DRUs) coupled to the one or more DAUs. The system also includes a dataprocessor coupled to the plurality of DRUs and comprising anon-transitory computer-readable storage medium comprising a pluralityof computer-readable instructions tangibly embodied on thecomputer-readable storage medium, which, when executed by the dataprocessor, provide for balancing of network traffic. The plurality ofinstructions include a) instructions that cause the data processor topartition the plurality of DRUs into a plurality of DRU sectors and b)instructions that cause the data processor to measure at least onesystem metric for traffic conditions associated with the plurality ofDRU sectors. The plurality of instructions also include c) instructionsthat cause the data processor to compare the at least one system metricto a predetermined threshold and d) instructions that cause the dataprocessor to determine that the at least one system metric is greaterthan the predetermined threshold. The plurality of instructions furtherinclude e) iterating at least elements (a) through (d). In someembodiments, depending on the particular system metric, the instructionscan be modified to determine that the at least one system metric is lessthan the predetermined threshold. In this case, iteration is performeduntil the system metric is greater than or equal to the predeterminedthreshold. One of ordinary skill in the art would recognize manyvariations, modifications, and alternatives.

In some embodiments, the number of iterations may depend on trafficconditions in at least one of the DRU sectors, such that therepartitioning satisfies at least one of a soft capacity constraint or ahard capacity constraint. In some embodiments, the soft capacityconstraint may include mitigating handoffs, and the hard capacityconstraint may include a maximum number of processed calls at any onepoint in time. In some embodiments, the dynamic repartitioning is basedon at least one optimization algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

An understanding of the nature and advantages of various embodiments maybe realized by reference to the following figures. In the appendedfigures, similar components or features may have the same referencelabel. Further, various components of the same type may be distinguishedby following the reference label by a dash and a second label thatdistinguishes among the similar components. If only the first referencelabel is used in the specification, the description is applicable to anyone of the similar components having the same first reference labelirrespective of the second reference label.

FIG. 1 is a schematic illustration of an example Distributed AntennaSystem (DAS) according to some embodiments.

FIGS. 2A and 2B illustrate an example methodology according to someembodiments.

FIG. 3 shows example sectorization problems and solutions according tosome embodiments.

FIGS. 4A and 4B are example sectorization scenarios associated with someembodiments.

FIGS. 5A and 5B are other example sectorization scenarios associatedwith some embodiments.

FIGS. 6A and 6B are yet other example sectorization scenarios associatedwith some embodiments.

FIGS. 7, 8, and 9 illustrate quantitative improvements according toimplementations of some embodiments.

FIGS. 10, 11, and 12 show charts of example tradeoffs between thepopulation size and the number of generation required to achieve aspecified performance according to some embodiments.

FIG. 13 shows an example flowchart according to some embodiments.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

3GPP LTE is a promising candidate for next generation wireless networks.In the last 15 years there has been substantial growth in cellularmobile communication systems. It is imperative to provide a high qualityof service (QoS) at a minimum cost. With the substantial increase incellular users, traffic hot spots and unbalanced call distributions arecommon in wireless networks. This decreases the quality of service andincreases call blocking and dropping. Inter-cell optimization in GSM andUMTS networks is usually delivered at the stage of network planning, andoften done manually. As traffic environments change, the networkperformance will not be optimum. It is therefore necessary to performinter-cell optimization of the network dynamically according to thetraffic environment, especially when cell traffic loads are notuniformly distributed. This is one of the important optimization issuesin self-organizing networks (SON) for 3GPP LTE. When the traffic loadsamong cells are not balanced, the blocking probability of heavily loadedcells may be higher, while their neighboring cells may have resourcesnot fully utilized. In this case load balancing can be conducted toalleviate and even avoid this problem. Additionally, othernext-generation networks besides 3GPP LTE may experience similarproblems. In some embodiments, the solutions presented herein may applyto those architectures as well.

According to some embodiments, one approach to solve this problem is totake advantage of aspects of a Distributed Antenna System (DAS). A DASbreaks the traditional radio base station architecture into two pieces:a central processing facility and a set of distributed antennas (DAs)connected to the central facility by a high-bandwidth network. The DASnetwork transports radio signals, in either analog or digital form,to/from the central facility where all the base stations processing isperformed. By replacing a single high-power antenna with severallow-power antennas distributed to give the same coverage as the singleantenna, a DAS is able to provide more-reliable wireless service withina geographic area or structure while reducing power consumption. In aVirtualized DAS, the traffic load from multiple sectorized eNodeBs, at ahotel station, is distributed to multiple Digital Remote Units (DRU).This synergistic configuration also allows multiple operators to usemultiple technologies over the same physical facilities, therebyproviding a cost-effective means of furnishing subscribers with highquality, highly reliable communication services. With respect to aVirtualized DAS configuration, multiple DRUs can be dynamically assignedto the different eNodeB sectors depending on the time-varying traffic,in order to resolve unbalanced traffic scenarios.

One of the primary design challenges for a Virtualized DAS system islocation area management. The location area management problem can begenerally stated as: For a given network of nDRUs, the objective is topartition the network into m coverage areas, without exceeding thenumber of users which can be supported by the eNodeBs. To provide thebest Quality of Service (QoS) for a given number of eNodeBs andresources, the call traffic must be dynamically balanced when weconsider call handoffs and call blockage rates. This is a locationmanagement optimization problem that can be accomplished throughdelivering the sectorized eNodeB traffic to the independent DRUs.

Once traffic resources are aggregated into eNodeB hotels, the discreteresources of a single eNodeB are still allocated to a specific set ofantennas associated with that eNodeB and providing coverage to aspecific geographic area. The traffic resources are fixed, i.e., onlythe resources associated with a specific eNodeB can be allocated to theantennas associated with that eNodeB. However, because the eNodeBs arecollocated in an eNodeB hotel, the system can use the aggregated trafficresources of the discrete eNodeBs as a single, pooled traffic resourcethat can be allocated according to various algorithms. Assumptions aretypically predicated on worst-case traffic assets in all areas, networkdesign is wasteful 99 percent of the time, inevitably resulting in over-or under-provisioning of fixed resources. Traffic resources either gounused (idle channels), or are under-provisioned and are insufficient tohandle the offered traffic. Both circumstances give rise to the sameoutcome: lost revenue and lost opportunity. When a site's trafficresources are idle and unused, the traffic asset fails to provide anoptimal return on investment. But a site that lacks sufficient capacityto support the offered traffic at any point during the day garnersdropped calls, lost revenue opportunity, and dissatisfied customers. Thetraffic information derived from an extensive sensor network will beused to dynamically allocate the traffic resources to the requiredgeographical areas only for the time period the service is needed. Oncethe service is supplied and the traffic sensor network determines thatthe traffic resources are no longer required, they are returned to theresource pool for reallocation. The entire network automaticallyreconfigures itself based on the perceived (sensed) need or in the eventof disruption due to natural or manmade events.

Load balancing can be classified into two general categories: blockprobability-triggered load balancing and utility based load balancing.However, very little research has dealt with the unbalance load networkscenario in LTE-like packet switched networks.

It may be known in the art various schemes related to mobile cellularnetworks and increasing system capacity. Balancing the traffic load anduse of DAS are two of the most important ones which have rarely beenconsidered in the art. Traffic load balancing in mobile cellular networkmay be well known since the first generation of mobile communicationsystems. Many methods have been proposed to address this problem, suchas cell splitting, channel borrowing, channel sharing, dynamical channelallocation, etc. The applications of DAS in cellular networks may alsobe known. However, most work related to traffic load balancing onlyfocuses on different radio resource allocation schemes, and most work onDAS only considers the radio propagation channels within a multi-cell toimprove the system capacity.

Geographic load balancing using VDAS, as described in some embodimentsof the present invention, is recognized as a new approach for trafficload balancing which provides dynamic load redistribution in real timeaccording to the current geographic traffic conditions. It can be usedto improve the performance for any distributed systems containingnon-uniformly distributed traffic, especially for resolving traffic hotspots. Knowledge in dynamic sectorization, use of tilted antennas, anddynamic cell-size control (cell breathing) illustrate that the systemperformance can be improved by balancing non-uniformly distributedtraffic.

Some embodiments of the present invention deliver the eNodeB traffic toindependent DRUs with the objective to dynamically balance the traffic.Sectorization also reduces handoffs between cells allocated to the samesector, which is possible with the simulcast capability inherent in aDAS network. This disclosed management of the sectorized DRUs in someembodiments is distinct from the existing balancing methods. In previousdynamic balancing research, fixed basic resources are allocated to eachcell and some reserved or borrowed resources are assigned to cells withhigher traffic. However, in disclosed embodiments, units called hardcapacities control resources and are assigned to eNodeBs by groupingDRUs depending on the time-varying traffic at each cell. Thus thedynamic sectorization that satisfies hard capacities dramaticallyreduces call blocking probability and call handoffs.

The remainder of this disclosure, all of which disclose certain aspectsof the present invention, is organized as follows. VirtualizedDistributed Antenna Systems and dynamic sectorization of DRUs. Aformulation for the DRU load-balancing problem. Two evolutionaryalgorithms to solve the problem: Genetic Algorithm (GA) and EstimationDistribution Algorithm (EDA). The performance of two algorithms iscompared with the optimal or lower bound solution.

Distributed Antenna System

Distributed antenna systems (DAS) have been widely implemented instate-of-the art cellular communication systems to cover dead spots. InDAS, antenna modules are geographically distributed to reduce accessdistance instead of centralizing at a location. Each distributed antennamodule is connected to a home base station (eNodeB hotel or centralunit) via dedicated wires, fiber optics, or an exclusive RF link.

From an architectural point-of-view, DAS has discernible advantages overconventional communication systems. DAS can reduce the systeminstallation costs and simplify maintenance, because DAS can reduce therequired number of base stations within a target service area. Analternative strategy is to try to reduce the overall transmit powerusing distributed antennas, which has the additional merit of providingbetter coverage and increased cellphone battery life. Althoughdistributed antennas systems (DAS) were originally introduced to simplycover the dead spots in indoor wireless communications, recent studieshave identified other potential advantages such as power and systemcapacity, and expanded its applications. Furthermore, blockingprobability can be improved owing to the principle of trunkingefficiency because resources for signal processing such aslicenses/channel cards are centralized and shared at the home basestation. In addition to these architectural advantages, DAS also hasbeen shown to possess advantages in terms of power, signal-tointerference-plus-noise ratio (SINR), and capacity owing tomacro-diversity and the reduced access distance. Based on theseadvantages, many cellular service providers or system manufacturers arereplacing legacy cellular systems with distributed antenna systems.However, most of the recent work on DAS has been focused oninvestigating those advantages and analyzing its performance. On theother hand, there are few disclosure on load balancing in time-varyingnetworks using DAS.

System Structure

FIG. 1 discloses certain embodiments of the present invention. In aVirtualized DAS as shown in FIG. 1, Digital Remote Antennas (DRUs), suchas DRU 110, are connected to an eNodeB hotel 100 via optical fiber, suchas optical fiber cable 112, and Digital Access Units (DAU), such as DAU104 (DAU 1). Each DRU may provide service to a cell, such that whoeveris within range of the cell will be serviced by the particular DRU inthe cell. The DRUs may be strategically installed so that the sum totalof the DRUs may provide service to a wide area, such as the 7-cell area106 as shown. In other cases, fewer DRUs may be installed, and mayprovide service to a smaller area, such as 3-cell area 108. The DAUs areinterconnected and connected to multiple sectors, such as Sectors 1, 2,and 3 in eNodeB1 102. This capability enables the virtualization of theeNodeB resources at the independent DRUs. The eNodeB hotel 100 may belinked to a public switched telephone network or a mobile switchingcenter. DRUs are sectorized such that each DRU allocated to a giveneNodeB sector is simulcast. For the simulcasting operation, the accessnetwork between the eNodeB hotel and DRUs should have a multi drop bustopology. The DAUs dynamically assign the radio resources of the varioussectors to the independent DRUs according to the traffic demand.

An eNodeB limitation for the number of active users is defined as hardcapacity. Base Station vendors charge the operators based on the numberof licenses (resources) allocated. In general, a three sector BTS, suchas eNodeB 102, shares the licenses for traffic demand amongst thesectors. As an example, 600 licenses could be assigned to the threesector BTS. The set of 600 licenses is called the virtual base station(VBS). An eNodeB hotel can contain several VBSs.

Dynamic DRU Sector Allocation

According to some embodiments, in the Virtualized DAS discussed inrelation to FIG. 1, it is beneficial to sectorize the DRUs to cope withdynamically changing traffic and to balance the traffic for each sector.At the DRU coverage area the traffic is increased or decreased dependingon the period of time. Thus embodiments provide methods and systems todynamically sectorize the DRUs such that the DRUs in a sector satisfythe soft capacity (e.g., the maximum number of users in order tomaintain an acceptable SNR) and the sectors in a VBS meet the hardcapacity (e.g., the total number of licenses/sources assigned to a VBS).Some embodiments focus on proper sectorization that satisfies the softand hard capacities for balanced traffic. Without proper sectorizationthere may be cases of unbalanced traffic where the number of blockedcalls are increased in a specific sector, even if the other sectors havevery little traffic. FIG. 2A shows an example of sectorization wherethere are ten DRUs, three sectors and one VBS. The sectors as shown maybe distinguished by the different orientation of lines within the DRUs.For example, the DRUs with horizontal lines constitute a first sector202, the DRUs with criss-crossed lines constitute a second sector 204,and the DRUs with vertical lines constitute a third sector 206. Thenumbers within each hexagonal DRU may represent a present number ofcalls at a given time. Thus, for example, sector 202 currently services80+70+50=200 calls in FIG. 2A. In this example, by assuming 600 licensesof hard capacity in the VBS and 200 channels of soft capacity persector, improper sectorization produces 35 blocked calls, while theother two sectors have idle channels. This can be seen by observing thatsector 206 has 50+45+80+60=235 calls at one time, while sector 204 has40+35+75=150 calls. Assuming each call has its own separate channel, andeach sector has a soft capacity of 200 channels, then sector 206 has 35more calls than it can presently handle, resulting in 35 dropped orblocked calls. Such a situation may be characterized as an impropersectorization, due to at least one sector being heavily loaded withcalls while other sectors still have capacity to handle more calls.

However, no calls are blocked and the traffic is well balanced in theproper sectorization. Referring to FIG. 2B, if the DRUs are balancedacross the sectors better, then no blocked calls may occur. In thiscase, the DRU from sector 206 with 50 calls may be reallocated to sector204 which had only 150 calls. Thus, a reallocation according to someembodiments results in balanced sectors 204′ (40+35+75+50=200) and 206′(45+80+60=185) serving no more than their channel soft capacity,resulting in no blocked or dropped calls. Now, note that all DRUsallocated to a given sector broadcast the radio signals in a simulcast.In some embodiments, the number of blocked calls may be a metric that isdesired to be minimized or otherwise optimized. For example, in someembodiments, if the number of blocked calls is currently above apredetermined threshold, then steps may be taken to reallocate DRUs toother sectors and to generally rebalance the load between sectorsthrough a series of iterations until the number of blocked calls fallsbelow the threshold.

In general, the present disclosures illustrate several problems that mayresult in inefficient and suboptimal sectorizations. For example, if theDRUs servicing these hexagonal areas are disconnected as in FIG. 3( a),there may be significant interference from other adjacent simulcastinggroups. Notice how DRUs 302, with horizontal lines, is in the samesector as DRU 304, also with horizontal lines, but they are clearlydisconnected. The disconnected sectorization also generates unnecessaryhandoffs between sectors. For example, if a user is on a call and passesfrom DRUs 302 over to DRU 304, the user will have to pass through someof the vertical-lined DRUs that are associated with a different sector.Each time the user crosses into a different sector, a handoff must takeplace, resulting in unnecessary handoffs and wasted resources.Therefore, it is better when cells allocated to a given sector areconnected. To minimize the handoffs and interference among sectors weconsider the compactness of DRU sectorization. FIG. 3( b) shows examplesof connected but in compact sectorization of DRUs. For example, sector206, 208, and 310, as distinguished by the different lined orientations,are each connected to DRUs within their respective sectors. However, theDRUs are grouped generally in a line formation. This may result in ahigher tendency to have to perform handoffs, because there is a largerperimeter around the sectors than if the DRUs were grouped in a morecompact manner. An isolated DRU surrounded by DRUs allocated to othersectors may experience higher interference than the DRUs in compactsectorization. Compact sectorization also reduces the number of handoffsby decreasing the length of the border between two different sectors.

In contrast, FIG. 3( c) shows a connected and compact configuration. Thesectors 312, 314, and 316 all have their respective DRUs connected, andthe configuration of the DRUs are more compact than say, FIG. 3( b)because there is a smaller perimeter around the sectors, resulting insmaller likelihood of users moving across the borders of the sectors andneeding to perform handoffs. In general, it may be a goal according tosome embodiments to generate sector configurations that are wellbalanced, in the sense that the number of blocked calls is minimized(i.e. proper sectorization), the number of handoffs may be minimized,and/or the configurations of the cells are connected and compact (whichrelates at least to minimizing the number of handoffs). Additionally,embodiments may be viewed as solving or mitigating a “soft-capacity”problem and/or a “hard-capacity” problem related to DRU sectorization.For example, a soft capacity constraint of the sectors may be the numberof handoffs occurring between the sectors. A hard capacity constraintmay be the number of channels each sector is allocated, therebydetermining a maximum number of calls within the sector at any point intime.

To measure the compactness of sectorization in the hexagonal DRUenvironment, introduced is the compactness index (CI) which is definedas the ratio of the number of handoff cell sides to the total number ofcell sides in a VBS. For example the CI of the sectorization of FIG. 3(b) is 10/18 and FIG. 3( c) is 7/18.

As will be discussed more below, embodiments of the present inventionmay perform dynamic repartitioning of DRUs into different sectors basedon at least one optimization algorithm in order to reduce, minimize,and/or optimize these and other similar types of problems. The algorithmmay involve measurement of various key performance indicators (KPI)including signal strength, which provides an indication of the number ofusers operating through a given DRU.

Formulation of DRU Sectorization

The network's performance (expressed by the number of KPIs fromdifferent parts of the network) determines the QoS values. Differentoperators may have different defined business goals and differentservices of interest. Based on these considerations, efficient and costeffective network performance management varies from operator tooperator. Therefore, QoS metrics could be defined and mapped to a set ofKPIs. When a set of KPIs is used, then the mapping needs to berepresented by a weighted normalized function.

The following describes a formulation of the sectorization problem withmixed integer programming to balance traffic among sectors and tominimize handoffs with connected and compact sectors. These formulationsare included in parts of embodiments of the present invention. Given thesectorization of DRUs at time period t, a problem is to obtain newsectorization at time period t+1 that adaptively balances the change intraffic demands.

In order to formulate the problem, consider a service coverage area withN DRUs. Each DRU is assumed to have traffic demand T_(i) i=1, . . . , N.Note that, UE_(A) belongs to DRU_(B) if the received uplink power fromUE_(A) at DRU_(B) is greater than the other DRUs. Let p_(ij) be thetransition probability of mobiles from DRU_(i) to DRU_(j). Then, thehandoff calls from DRU_(i)to DRU_(j) become h_(ij)=p_(ij)T_(i). Thedistance between DRU_(i) and DRU_(j) is inversely proportional top_(ij). Assume that an eNodeB hotel has M VBSs. Let SOS_(m) and SOD_(k)be the set of sectors in VBS_(m) and the set of DRUs allocated toSector_(k), respectively, such that |SOS_(m)|=3 (if each eNodeB/VBS hasthree sectors), m=1, . . . , M and k=1, . . . , K. Now consider thefollowing three cost factors (KPIs) in the sectorization problem:

(1) KPI_(BC) (Inverse of the Number of Blocked Calls): The penalty ofthe blocked calls caused by hard capacity and soft capacity. Let HC_(m)and SC_(k) be the hard capacity of VBS_(m) and soft capacity ofSector_(k) respectively such that

${HC}_{m} = {\sum\limits_{k \in {SOS}_{m}}{{SC}_{k}.}}$

The binary variable x_(ik)=1, when DRU_(B) belongs to Sector_(k),

${y_{im} = {\sum\limits_{k \in {SOS}_{m}}x_{ik}}},$

then y_(im)=1 when DRU_(i) belongs to VBS_(m).

$\begin{matrix}{{sc}_{k} = \{ {\begin{matrix}0 & {{{if}\mspace{14mu} {\sum\limits_{i}^{\;}\; {T_{i}x_{ik}}}} < {SC}_{k}} \\{{\sum\limits_{i}^{\;}\; {T_{i}x_{ik}}} - {SC}_{k}} & {otherwise}\end{matrix}\begin{matrix}{{hc}_{m} = {{\sum\limits_{i}^{\;}\; {T_{i}y_{im}}} - {HC}_{m}}} \\{= {{\sum\limits_{i}^{\;}\; {T_{i}{\sum\limits_{k \in {SOS}_{m}}^{\;}\; x_{ik}}}} - {\sum\limits_{k \in {SOS}_{m}}^{\;}\; {SC}_{k}}}} \\{= {{\sum\limits_{k \in {SOS}_{m}}^{\;}{\sum\limits_{i}^{\;}\; {T_{i}x_{ik}}}} - {SC}_{k}}} \\{= {\sum\limits_{k \in {SOS}_{m}}^{\;}{sc}_{k}}}\end{matrix}} } & (1)\end{matrix}$

Since a penalty occurs only when the calls are blocked, we apply justsc_(k) to the objective function, because the hc_(m) is a function ofsc_(k) and there is no need to add it as another term to the objectivefunction. sc_(k) is a nonnegative real variable. So,

$\begin{matrix}{{KPI}_{BC} = \lbrack {\sum\limits_{k \in {SOS}_{m}}^{\;}{sc}_{k}} \rbrack^{- 1}} & (2)\end{matrix}$

(2) KPI_(HO) (Inverse of the Number of Handoffs): Consider threedifferent types of Handoffs:

(A) Inter-eNodeB Handoff: When user equipment (UE) with an ongoing callmoves from one VBS to another VBS, then the UE needs an inter-eNodeBhandoff. Inter-eNodeB handoff is executed using the X2 interface (aslong as the UE does not leave the LTE coverage area) or S1 interface(when the UE leaves the serving cell). The X2 handoff includesestablishing a signaling connection using the X2 Application Part (X2AP)from the source to the target eNodeB. The target eNodeB updates the MME(Mobility Management Entity) with the new geographic position of the UE.To enable this procedure, the MME needs to communicate with S-GW(Serving Gateway) to negotiate the new endpoint. During the S1 handoff,the MME receives the relocation preparation request of the sourceeNodeB, it starts the handoff resource allocation procedure to requestthe necessary radio resource from the target eNodeB. After the targeteNodeB sends the required radio interface parameters embedded in ahandoff command message; the MME forwards this handoff command messagetransparently to the UE, which executes the handoff. The main procedureis triggered by the MME and executed by S-GW.

Let the binary variable z_(ijm)=1, when DRU_(i) and DRU_(j) belong toVBS_(m). Then the inter-eNodeB handoff cost is computed by using thevariable

${z_{ij} = {1 - {\sum\limits_{m}^{\;}\; z_{ijm}}}},$

the cost becomes

$\sum\limits_{i}^{\;}\; {\sum\limits_{j}^{\;}\; {h_{ij}{z_{ij}.}}}$

Note that inter-eNodeB handoff occurs when DRU_(i) and DRU_(j) belong todifferent VBS, i.e., z_(ijm)=0.

(B) Intra-eNodeBHandoff: When a UE with an ongoing call moves from onesector to another in a VBS, then the mobile needs an intra-eNodeBhandoff. This procedure doesn't need to involve MME or S-GW because itcan be handled entirely within that VBS. Now by letting the binaryvariable w_(ijk)=1 when DRU_(i) and DRU_(j) belong to sector k, theintra-eNodeB handoff cost is computed by using two variablesw_(ij)−z_(ij) where

${w_{ij} = {1 - {\sum\limits_{k}^{\;}\; w_{ijk}}}},$

the cost becomes

$\sum\limits_{i}^{\;}\; {\sum\limits_{j}^{\;}\; {{h_{ij}( {w_{ij} - z_{ij}} )}.}}$

An intra-eNodeB handoff occurs when DRU_(i) and DRU_(j) belong todifferent sectors of the same VBS.

(C) Forced Handoff: When a DRU changes its sector, all ongoing calls inthe cell have to change their pilot PN offsets for WCDMA. The cost offorced handoff is computed by employing the current sectorizationa_(ik), which is equal to zero when DRU_(i) is in Sector_(k). Since thecost occurs when DRU_(i) currently in another sector moves intoSector_(k), the cost becomes

$\sum\limits_{i}^{\;}\; {\sum\limits_{k}^{\;}\; {a_{ik}T_{i}{x_{ik}.}}}$

The weighted combination of these three handoff cost would be:

$\begin{matrix}{{KPI}_{HO} = \lbrack {{c_{1}{\sum\limits_{i}^{\;}\; {\sum\limits_{j}^{\;}\; {h_{ij}z_{ij}}}}} + {c_{2}{\sum\limits_{i}^{\;}\; {\sum\limits_{j}^{\;}\; {h_{ij}( {w_{ij} - z_{ij}} )}}}} + {c_{3}{\sum\limits_{i}^{\;}\; {\sum\limits_{k}^{\;}\; {a_{ik}T_{i}x_{ik}}}}}} \rbrack^{- 1}} & (3)\end{matrix}$

(3) KPI_(CI) (Inverse of the Compactness Index): The object here is tominimize the length of handoff border with the compactness index CI. InEquation (4) the numerator term represents the number of handoff DRUsides between two different sectors.

$\begin{matrix}{{KPI}_{CI} = {\lbrack{CI}\rbrack^{- 1} = \lbrack \frac{\sum\limits_{i}^{\;}\; {\sum\limits_{i \prec j}^{\;}\; {w_{ij}B_{ij}}}}{\sum\limits_{i}^{\;}\; {\sum\limits_{i \prec j}^{\;}\; B_{ij}}} \rbrack^{- 1}}} & (4)\end{matrix}$

Where B_(ij)=1, if DRU_(i) and DRU_(j) are adjacent.

Now consider the following constraints required in the formulationincluded in various embodiments of the invention:

1. each DRU has to belong to a sector, that is,

$\begin{matrix}{{\sum\limits_{k}^{\;}\; x_{ik}} = {1\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} i}} & (5)\end{matrix}$

2. The relationship between any two DRUs in a Sector_(k) has to satisfyw_(ijk)=1 if and only if

x _(ik) =x _(jk)=1. Thus:

w _(ijk) ≦x _(ik) ,w _(ijk) and w _(ijk) ≧x _(ik) +x _(jk)−1 for all i,jand k  (6)

3. The relationship between two DRUs in a VBS_(m)·z_(ijm)=1 if and onlyif

y _(im) =y _(jm)=1 which leads to:

z _(ijm) ≦y _(im) ,z _(ijm) ≦y _(jm) and z _(ijm) ≧y _(im) +y _(jm)−1for all i,j and k  (7)

4. Connected sectorization, if a sector has more than one DRU, then theDRUs of the sector have to be connected. For the formulation ofconnected sectors we employ the cut theorem on SOD_(k). If Sector_(k) isconnected, then any cut that separates cells in SOD_(k) has at least onecommon side of the hexagonal cells. Let S1_(k) be a proper subset ofSOD_(k), that is, S1_(k)⊂SOD_(k), S1_(k)≠φ, and S1_(k)≠SOD_(k). Also letS2_(k) be the opposite set of S1_(k), that is, S2_(k)=SOD_(k)−S1_(k).Because two subsets are connected, there exists at least one common sideof the DRUs separated by the subsets. In other words:

$\begin{matrix}{{\sum\limits_{i \in {S\; 1_{k}}}^{\;}\; {\sum\limits_{j \in {S\; 2_{k}}}^{\;}\; B_{ij}}} \geq 1} & (8)\end{matrix}$

Now, the QoS function is the weighted combination of three KPIs (costfactors) which have already been introduced, above. Obviously theobjective function is to maximize the QoS function. There are penaltiesof blocked calls by hard and soft capacities and handoff calls. The DRUsectorization can be formulated as the following mixed integer linearprogramming:

Minimize QoS⁻¹ = w₁ ⋅ KPI_(BC)⁻¹ + w₂ ⋅ KPI_(HO)⁻¹ + w₃ ⋅ KPI_(CI)⁻¹Subject  to:${\sum\limits_{k}^{\;}\; x_{ik}} = {1\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} i}$w_(ijk) ≤ x_(ik), w_(ijk) ≤ x_(jk) andw_(ijk) ≥ x_(ik) + x_(jk) − 1  for  all  i, j  and  k${w_{ij} = {1 - {\sum\limits_{k}^{\;}\; {w_{ijk}\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} i}}}},j$$y_{im} = {\sum\limits_{k \in {SOS}_{m}}^{\;}\; {x_{ik}\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} m}}$z_(ijm) ≤ y_(im), z_(ijm) ≤ y_(jm) andz_(ijm) ≥ y_(im) + y_(jm) − 1  for  all  i, j  and  m${z_{ij} = {1 - {\sum\limits_{k}^{\;}\; {z_{ijk}\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} i}}}},j$${{\sum\limits_{i \in {S\; 1_{k}}}^{\;}\; {\sum\limits_{j \in {S\; 2_{k}}}^{\;}\; B_{ij}}} \geq {1\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} S\; 1_{k}}} \Subset {SOD}_{k}$where S 1_(k) ≠ φ and S 1_(k) ≠ SOD_(k) andS 2_(k) = SOD_(k) − S 1_(k)h_(ij) = p_(ij)T_(i)  for  all  i  and  j${hc}_{m} = {{\sum\limits_{i}^{\;}\; {T_{i}y_{im}}} - {{HC}_{m}\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} m}}$${sc}_{k} = \{ {{\begin{matrix}0 & {{{if}\mspace{14mu} {\sum\limits_{i}^{\;}\; {T_{i}x_{ik}}}} < {SC}_{k}} \\{{\sum\limits_{i}^{\;}\; {T_{i}x_{ik}}} - {SC}_{k}} & {otherwise}\end{matrix}\mspace{14mu} {for}\mspace{14mu} {all}\mspace{14mu} kx_{ik}},w_{ijk},z_{ijm},{P_{k} \in {\{ {0,1} \} {for}\mspace{14mu} {all}\mspace{14mu} i}},j,{k\mspace{14mu} {and}\mspace{14mu} m}} $

Note that many grouping problems which are special cases of thesectorization problem are well-known NP-hard problems. Since our problemis NP hard, the time it takes to execute the algorithm is exponentiallyincreasing with the size of the problem. Such an algorithm is thus inmost cases unusable for real-world size problem. As an encouragingresult on NP-hard problems, we investigate evolutionary algorithms tosolve the sectorization problem and compare the performance with thesolutions obtained by the mixed integer programming.

Evolutionary Algorithm

Evolutionary algorithms have been used to solve difficult optimizationproblems. Some embodiments employ evolutionary algorithms to optimizethe network traffic in the system. Candidate solutions to anoptimization problem are represented as individuals in the population.Evolutionary Algorithms (EAs) are inspired by the theory of biologicalevolution. In EAs the cost function value of a candidate solution to theoptimization problem indicates the fitness of the individual in theconcept of natural selection. In the following, two types ofEvolutionary algorithms are discussed in relation to solving the problemformulated above: Genetic Algorithm and Estimation DistributionAlgorithm.

A. Genetic Algorithm (GA)

GAs are stochastic search methods that mimic genetic phenomena such asgene recombination, mutation and survival of the fittest. GAs have beenapplied to a large number of scientific and engineering problems,including many combinatorial optimization problems in networks. GAsoperate on a set of candidate solutions, called a population. Eachsolution is typically represented by a string, called a chromosome. Eachchromosome is assigned a fitness value that measures how well thechromosome solves the problem at hand, compared with other chromosomesin the population. From the current population, a new population isgenerated typically using three genetic operators: selection, crossoverand mutation. Chromosomes for the new population are selected randomly(with replacement) in such a way that fitter chromosomes are selectedwith higher probability. For crossover, survived chromosomes arerandomly paired, and then two chromosomes in each pair exchange a subsetof their strings to create two offspring. Chromosomes are then subjectto mutation, which refers to random flips of the string's elementapplied individually to each of the new chromosomes. The process ofevaluation, selection, crossover and mutation forms one generation inthe execution of a GA. The above process is iterated with the newlygenerated population successively replacing the current one. The GAterminates when a certain stopping criterion is reached, e.g., after apredefined number of generations. There are several aspects of thisproblem suggesting that a GA-based method may be a promising candidateand are thus used in some embodiments: GA has proven to work well if thespace to be searched is large, but known not to be perfectly smooth, orif the space is not well understood, and if finding a global optimum isnot critical. In general, they use a penalty function to encode problemconstraints and allow a search for illegal solutions, e.g., a solutionthat violates the connectedness or compactness of DRUs in thissectorization problem. Allowing a search for illegal solutions mayprevent falling down into a local minimum and generate a bettersolution. During each generation of the GAs individuals in the currentpopulation are rated for their fitness as domain solutions. The fitnessvalue is based on the objective function value of Equation (9).

In general, conventional GAs can be characterized by parameters andnotations

(I _(s) ,F,Δ _(l),η_(l) ,P _(s) ,Pc,Pm,I _(Ter))  (10)

where1) I_(s)is the space of all potential solutions (entire search space ofchromosomes).2) F denotes a fitness function.3) Δ_(l) is the set of chromosomes(population) at the l_(th) generation.4) η_(l) is the set of best candidate solutions selected from set Δ_(l)at the l_(th) generation.5) We denote β_(l)≡Δ_(l)−η_(l)≡Δ_(l)∩η_(l) ^(C), where η_(l) ^(C) is thecomplement of n_(l).6) p_(s) is the selection probability. The GA algorithm selectsp_(s)|Δ_(l)| individuals from set Δ_(l) to make up set η_(l).7) Pc is the crossover probability. In the uniform crossover process twochromosomes are randomly chosen with a probability Pc and genes areexchanged with a rate CR.8) Pm is the mutation probability. For the mutation a gene is randomlychosen with a probability Pm and the value of the gene is changed.9) I_(Ter) are the maximum number of generation.

In conventional GAs, each chromosome is designated by a string ofdifferent gene with length n (n-dimensional vector). A typical GA isdescribed in the following steps:

Step 0: Generate initial population Δ_(o). The initial population(|Δ₀|chromosomes) is typically obtained by sampling according theuniform (equally likely) distribution.Step 1: Evaluate the chromosomes in the current population Δ_(l-1)according to the fitness function F. Sort the candidate solutions(chromosomes in the current population) according to their fitnessorders.Step 2: If the best candidate solution satisfies the convergencecriterion or the number of iterations exceeds its limit I_(Ter), thenterminate; else go to step 3.Step 3: Select the best p_(s)Δ_(l-1) candidate solutions (chromosomes)from current population Δ_(l-1). This selection is accomplishedaccording to the sorted candidate solutions.Step 4: Perform uniform crossover and flipping mutation and thengenerate new |Δ_(l-1)|−|η_(l-1)| chromosomes on the basis of theseperforming. Replace the bad 1|β_(l-1)| chromosomes with newly generated|Δ_(l-1)|−|η_(l-1)| chromosomes.Step 5: Go to step 1 and repeat the steps

B. Estimation Distribution Algorithm (EDA)

Unlike other evolutionary algorithms, in EDA a new population ofindividuals in each generation is generated without crossover andmutation operators. Instead, in EDA a new population is generated basedon a probability distribution, which is estimated from the best selectedindividuals of previous generation. We introduce each main-vector as anindividual for the EDA approach to embodiments of the present invention,and also the fitness function is objective function. In general,conventional EDAs can be characterized by parameters and notations

(I _(s) ,F,Δ _(l),η_(l),β_(l) ,p _(s),Γ,I_(Ter))  (11)

where1) I_(s) is the space of all potential solutions (entire search space ofindividuals).2) F denotes a fitness function.3) Δ_(l) is the set of individuals (population) at the l_(th)generation.4) η_(l) is the set of best candidate solutions selected from set Δ_(l)at the l_(th) generation.5) We denote β_(l)≡Δ_(l)−η_(i)≡Δ_(l)∩η_(l) ^(C), where η_(l) ^(C) is thecomplement of η_(l).6) p_(s) is the selection probability. The EDA algorithm selectsp_(s)|Δ_(l)| individuals from set Δ_(l) to make up set η_(l).7) We denote by Γ the distribution estimated from η_(l) (the set ofselected candidate solutions) at each generation.8) I_(Ter) are the maximum number of generation

In conventional EDAs, each individual is designated by a string. Atypical EDA is described in the following steps:

Step 0: Generate initial population Δ₀. The initial population (|Δ₀|individuals) is typically obtained by sampling according the uniform(equally likely) distribution:

$\begin{matrix}{\mspace{79mu} {{{{{p( {\theta_{1},\theta_{2},\ldots \mspace{14mu},\theta_{n}} )} = {\prod\limits_{i = 1}^{n}\; {p_{i}( \theta_{i} )}}},\mspace{79mu} {{\forall i} = 1},2,\ldots \mspace{14mu},n,\mspace{79mu} {and}}{p_{i}( {\theta_{i} = S_{11}} )}} = {{p_{i}( {\theta_{i} = S_{21}} )} = {\ldots = {{p_{i}( {\theta_{i} = S_{{{SOS}_{M}}M}} )} = \frac{1}{\sum\limits_{n = 1}^{M}\; {{SOS}_{n}}}}}}}} & (12)\end{matrix}$

For generation l=1, 2, . . . . , follow steps 1 through 6Step 1: Evaluate the individuals in the current population Δ_(l-1)according to the fitness function F. Sort the candidate solutions(individuals in the current population) according to their fitnessorders.Step 2: If the best candidate solution satisfies the convergencecriterion or the number of generation exceeds its limit I_(Ter), thenterminate; else go to step 3.Step 3: Select the best p_(s)Δ_(l-1) candidate solutions (individuals)from current population Δ_(l-1). This selection is accomplishedaccording to the sorted candidate solutions.Step 4: Estimate the probability distribution p(θ₁, θ₂, . . . , θ_(n))on the basis of |η_(l-1)| best candidate solutions. This estimation isdenoted by

Γ=P(θ₁,θ₂, . . . , θ_(n)|η_(l-1))  (13)

Step 5:Generate new |Δ_(l-1)|−|η_(l-1)| individuals on the basis of thisnew estimated probability distribution Γ. Replace the bad |β_(l-1)|individuals with newly generated |Δ_(l-1)|−|η_(l-1)| individuals.Step 6: Go to step 1 and repeat the steps

Embodiments of the present invention follow the steps of the abovepseudo code in the EDA implementations. For estimation, a simple schemeof estimating the marginal distributions separately is used, and usingproduct form

$\begin{matrix}{= \; {{p( {\theta_{1},\theta_{2},\ldots \mspace{14mu},{\theta_{n}\eta_{l - 1}}} )} = {{\prod\limits_{i = 1}^{n}\; {p_{i}( {\theta_{i}\eta_{l - 1}} )}} = {\prod\limits_{i = 1}^{n}( \frac{\sum\limits_{j = 1}^{\eta_{l - 1}}\; {\delta ( {x_{i}^{j} = {\theta_{i}\eta_{l - 1}}} )}}{\eta_{l - 1}} )}}}} & (14)\end{matrix}$

Where δ is an indicator function and it can be expressed as

$\begin{matrix}{{\delta ( {x_{i}^{j} = {\theta \eta_{l - 1}}} )} = \{ \begin{matrix}1 & {{{if}\mspace{14mu} x_{i}^{j}} = \theta} \\0 & {otherwise}\end{matrix} } & (15)\end{matrix}$

The efficiency of the three algorithms for the DRU sectorization problemhas been analyzed in accordance with various embodiments of the presentinvention. Traffic is assumed uniformly distributed over the DRUs. Thenumber of VBSs and sectors are assumed as in Table I. The possiblenumber of sectorizations increases exponentially with the number ofDRUs. The performance of proposed algorithms will be investigated bycomparing with each other and optimal solutions of the formulation.

The cost coefficients employed for the objective function in Equation(9) are w₁=w₃=10, w₂=1, c₁=2, and c₂=c₃=1. Higher weights are given tow_(i) and w₅, in order to minimize the blocked calls by hard and softcapacity and CI is the first priority in the sectorization. The weightby the inter-eNodeB handoff w₁ is twice that of the intra-eNodeB handoffw₂ because handoff between two sectors on the same eNodeB can be handledentirely within that eNodeB, while handoff between two eNodeBs involvesnot only those eNodeBs, but also the MME and S-GW as well; this lattercase is much more expensive, since it involves more signaling, moreprocessing, more coordination, and more time. However, since the forcedhandoff only occurs at the beginning of sectorization period, equalweights are given to w₂ and w₃. Presented are the optimal solutions forthe 19, 37 and 61 DRUs problems using MATLAB simulations to validate theperformance of certain embodiments.

Parameters for Algorithms

Before investigating the performance of the algorithms for thesectorization problem, declared are tunings of the algorithmsparameters.

In GA Algorithm, each gene in a chromosome represents the sector and VBSto which the corresponding DRUs belong. We denote by a row vector X=(x₁,x₂, . . . , x_(n)), x_(i)ε{S_(jk)|j=1, . . . , |SOS_(k)|, k=1, . . . ,M}, i=1 . . . n as a chromosome where x_(i)=S_(jk) means DRU_(i) belongsto VBS_(k) and Sector_(j). A GA maintains a population of chromosomes ineach generation. We denote by |Δ_(l)| the number of chromosomes inpopulation Δ_(l). Where superscript j in the row vector x_(j)m(x₁ ^(j),x₂ ^(j), x₃ ^(j), . . . , x_(n) ^(j)) indexes an individual in thepopulation. In the uniform crossover process two chromosomes arerandomly chosen with a probability P_(c)=0.9 and gene are exchanged witha rate 0.4. For the mutation a gene is randomly chosen with aprobability P_(m)=0.1 and the value of the gene is changed, that is, theDRU changes its sector. The selection probability is P_(s)=0.5.

In EDA Algorithm, each individual is designated by a string, members ofwhich are taken from the set S, i.e. {S_(jk)|j=1, . . . , |SOS_(k)|,k=1, . . . , M} with length n (n-dimensional vector) where S_(jk) meansDRU_(i) belongs to VBS_(k) and Sector_(j). Each member in an individualrepresents the sector and VBS to which the corresponding DRUs belong. Wedenote by a row vector X=(x₁, x₂, . . . , x_(n)), x_(i)εS, i=1 . . . nas an individual. An EDA maintains a population of individuals in eachgeneration. We denote by |Δ_(l)| the number of individuals in populationΔ_(l). Where superscript j in the row vector x^(j)=(x₁ ^(j), x₂ ^(j), x₃^(j), . . . , x_(n) ^(j)) indexes an individual in the population. Theselection probability is P_(s)=0.5.

Generating the Initial Population

Generally in both algorithms, the initial population is chosen randomlyfrom set S with uniform distribution. It should be noted that it ispossible to get all infeasible solutions or very small number offeasible solutions when the size of the population in GA or EDA is muchsmaller than the size of the whole search space. To overcome thisproblem, it is necessary to have some percentage (e.g., x %) of theinitial population to be exactly in the form of sectorizing, on whichthe algorithm will be applied. In our algorithms we consider x=30.

Performance of the Algorithms

Referring to FIGS. 4A, 4B, 5A, 5B, 6A, and 6B, we now consider threebenchmarking problems illustrating various techniques and resultsaccording to some embodiments. In these cases, for the trafficdistribution, we use an Erlang distribution with average traffic of 50for all problems. We found the optimal solutions of the 19DRU problemusing MATLAB to validate the performance of the heuristics. We terminatethe algorithms after 35, 200 and 200 generations in 19_DRU (FIGS. 4A and4B), 37_DRU (FIGS. 5A and 5B) and 61_DRU (FIGS. 6A and 6B) scenarios,respectively. We define the convergence rate to be the number of timesan optimal (or best found for the 37 and 61 DRUs problems) solution isobtained over the overall number of generations performed. Each of theexamples shown, e.g. FIGS. 4A, 4B, 5A, 5B, 6A, and 6B, represent justone example problem and solution with respect to a certain size andscale of a number of DRUs to be balanced. These are merely justexamples, as it should be clearly seen that many of the characteristicsin each example can vary, e.g., the number of calls within each DRU, thenumber of DRUs initially in a sector, the number of sectors within eachVBS, the number of VBSs, the soft capacity of each sector, and the like,all of which are possible according to some embodiments. Table I shows abrief summary of the different scenarios presented in FIGS. 4A, 4B, 5A,5B, 6A, and 6B:

TABLE I Description of three benchmarking examples 19_DRUs 37_DRUs61_DRUs Number of DRUs 19 37 61 Number of VBSs/eNBs 2 3 4 Number ofSectors 6 9 12

For the 19_DRU problem, for example, as shown in FIGS. 4A and 4B, theevaluation QoS values of the old and the new groupings at times t andt+1 are 1.1*10⁻³(KPI_(BC) ⁻¹=90, KPI_(HO) ⁻¹=0, CI= 22/42) and3.488*10⁻³ (KPI_(BC) ⁻¹=0, KPI_(HO) ⁻¹=155, CI= 23/42) as shown in FIG.4A. The key 400 shows two VBSs with 3 sectors each in order toconstitute the 19-cell DRU configuration as shown. We find an optimalsolution with evaluation value of 286.904 using MATLAB with executiontime=8.64*10⁵ CPU seconds (around 9 days). The convergence rate of theEDA to this optimal solution is 0.93 (over 200 generations) with 0.082CPU seconds while the convergence rate of GA is 0.89 (over 200generations) with 0.0901 CPU seconds (cross-reference to FIG. 7.). Theresulting optimal solution is shown in FIG. 4B.

For the 37_DRU problem, for example, as shown in FIGS. 5A and 5B, theevaluation QoS values of the old and the new groupings at times t andt+1 are 0.5*10⁻³(KPI_(BC) ⁻¹=177, KPI_(HO) ⁻¹=0, CI= 56/90) and2.4*10⁻³(KPI_(BC) ⁻¹=7, KPI_(HO) ⁻¹=194, CI= 56/90) as shown in FIGS. 5Aand 5B. The key 500 shows three VBSs with 3 sectors each in order toconstitute the 37-cell DRU configuration as shown. This problem may becomputational intensive, so we compare the performance of the EDA andthe GA using convergence rate within limited CPU time. The convergencerate of the EDA to the best found solution is 0.87 with 0.1532 CPUseconds while the convergence rate of GA is 0.50 with 0.169 CPU seconds(cross-reference to FIG. 8.). The resulting improved solution is shownin FIG. 5B.

For the large 61_DRU problem, for example, as shown in FIGS. 6A and 6B,the evaluation QoS values of the old and the new groupings at times tand t+1 are 0.77*10⁻³(KPI_(BC) ⁻¹=128, KPI_(HO) ⁻¹=0, CI= 89/156) and2.8*10⁻³(KPI_(BC) ⁻¹=13, KPI_(HO) ⁻¹=105, CI= 77/156) as shown in FIGS.6A and 6B. The key 600 shows four VBSs with 3 sectors each in order toconstitute the 61-cell DRU configuration as shown. This problem may alsobe computational intensive, so we compare the performance of the EDA andthe GA using convergence rate within limited CPU time. The convergencerate of the EDA to the best found solution is 0.88 with 0.2815 CPUseconds while the convergence rate of GA is 0.49 with 0.3012 CPU seconds(cross-reference to FIG. 9.).

FIGS. 7, 8 and 9 show example performance results using EDA and GAalgorithms according to some embodiments. The results may show that notonly does EDA significantly outperform GA in its convergence rate to thebest solution, but also even the non-optimal solutions are much closerto the best solution as compared to GA. However, in both cases, the DRUsare significantly more efficient and better balanced than if notechniques according to embodiments were performed at all.

Specifically, referring to FIG. 7, chart 700 shows the quality ofservice (QoS %) metric between the EDA and GA algorithms for the 19-DRUproblem, for example as discussed in FIGS. 4A and 4B. In this case, bothEDA and GA algorithms seem to reach the same level of QoS, but GA seemsto reach it quicker through an earlier number of generations. Chart 710shows a number of block calls metric between the EDA and GA algorithmsfor the 19-DRU problem. As shown, the number of blocked calls isvirtually eliminated by implementing techniques of some embodiments.Chart 720 shows the number of handoffs occurring using both EDA and GAalgorithms. Here, both algorithms generally result in an 80% reduction,or a five-fold improvement, in the number of handoffs compared to usingno techniques according to embodiments of the present invention.

Referring to FIG. 8, chart 800 shows the quality of service (QoS %)metric between the EDA and GA algorithms for the 37-DRU problem, forexample as discussed in FIGS. 5A and 5B. In this case, the EDA algorithmseem to reach a higher level of QoS, while it can be clearly seen thatboth methods drastically improve the QoS compared to if no techniqueswere performed. Chart 810 shows a number of block calls metric betweenthe EDA and GA algorithms for the 37-DRU problem. As shown, the numberof blocked calls is drastically reduced by implementing techniques ofsome embodiments. Chart 820 shows the number of handoffs occurring usingboth EDA and GA algorithms. Here, both algorithms generally result in an75% reduction, or a four-fold improvement, in the number of handoffscompared to using no techniques according to embodiments of the presentinvention.

Referring to FIG. 9, chart 900 shows the quality of service (QoS %)metric between the EDA and GA algorithms for the 61-DRU problem, forexample as discussed in FIGS. 6A and 6B. In this case, the EDA algorithmseem to reach a higher level of QoS, while it can be clearly seen thatboth methods drastically improve the QoS compared to if no techniqueswere performed. Chart 910 shows a number of block calls metric betweenthe EDA and GA algorithms for the 61-DRU problem. As shown, the numberof blocked calls is drastically reduced by implementing techniques ofsome embodiments, although the EDA algorithm seems to result in evenfewer blocked calls. Chart 920 shows the number of handoffs occurringusing both EDA and GA algorithms. Here, both algorithms generally resultin a 93% reduction, or a fourteen-fold improvement, in the number ofhandoffs compared to using no techniques according to embodiments of thepresent invention.

Obviously, these two algorithms are highly likely to yield a bettersolution if the algorithm runs more generations. Also, with the samenumber of generation, the algorithms are likely to produce a bettersolution if a larger population size is used in each generation. Thereis a tradeoff between the population size and the number of generationrequired to achieve a specified performance of the final solution. FIGS.10, 11 and 12 show this trade off in three different scenarios solvedusing EDA. Specifically, FIG. 10 corresponds to the 19-DRU problem, FIG.11 corresponds to the 37-DRU problem, and FIG. 12 corresponds to the61-DRU problem. One can use this trade off to configure the algorithmsto available hardware; e.g., for hardware with a large memory, a largepopulation size is suitable.

Referring to FIG. 13, flowchart 1300 shows an example methodologyaccording to some embodiments. These descriptions may be consistent withany of the previous figures and descriptions discussed herein. The stepsin flowchart 1300 may be implemented by various components discussed inFIG. 1, for example.

At block 1302, in some embodiments, the method may provide a DAUassociated with a plurality of sectors of a base station. Examples ofthis provisioning may be shown in FIG. 1, for example. At block 1304,the method may provide a plurality of DRUs associated with the DAU. TheDRUs may be located in physically distinct areas from each other, suchas what is illustrated in FIGS. 1, 2A, 2B, and 3.

At block 1306, the plurality of DRUs may be partitioned into a pluralityof DRU sectors. In some embodiments, the DRU sectors may each beassociated with a distinct sector of the base station. In someembodiments, a plurality of base stations may be provided, wherein eachbase station has a plurality of sectors. In some embodiments, the DRUsmay be partitioned into connected sectors. In some embodiments, thepartitions may be substantially compact. The descriptions may beconsistent with the concepts discussed in FIGS. 2A, 2B and 3, forexample.

At block 1308, at least one system metric for traffic conditions may bemeasured. The system metric may be associated with the plurality of DRUsectors. In some embodiments, the at least one system metric may be anumber of blocked calls in the DRU sector. In some embodiments, the atleast one system metric may be a number of handoffs occurring along theborder of the DRU sector. In some embodiments, the at least one systemmetric may be a QoS measurement.

At block 1310, the at least one system metric may be compared to apredetermined threshold. For example, the threshold may be 200 calls ina DRU sector, consistent with examples discussed in FIGS. 2A, 2B, and 3.Clearly, other thresholds may be used, including those discussed inrelation to any of the other figures in the present disclosures.

At block 1312, it may be determined whether the at least one systemmetric is greater than the predetermined threshold. For example, thepresent number of total calls may be determined to be higher than thethreshold 200 calls. If it is determined to be greater than thethreshold, the method may iterate through the flowchart 1300 again,cycling back to block 1306. In the next iteration, the partitioning inblock 1306 may be different than before. In some embodiments, thepartitioning may be made according to an optimization algorithm, forexample, any of the algorithms described in the present disclosures.

If it is determined that the system metric is lower than the threshold,then the method may end, for it may be determined that the system is ina balanced or properly sectorized state. In some embodiments, the methodmay continue to monitor traffic, and may cycle back to block 1306whenever it is determined that the at least one system metric is greaterthan the predetermined threshold. It should be noted that the examplemethod illustrated in FIG. 13 is suitable for repartitioning in order todrive the system metric to a level below the predetermined threshold. Anexample could be dynamic repartitioning through iteration to reduce thenumber of blocked calls to zero. In other implementations,repartitioning can be performed to drive the system metric to a valuegreater than a predetermined threshold. An example could be the QoS,which can be increased as iteration is performed to reparation the DRUsectors. It will be appreciated that the inverse of the metric couldalso be employed depending on whether the comparison in process 1312 isthat the metric is greater than or less than the threshold. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

Various examples have been described. These and other examples arewithin the scope of the following claims.

What is claimed is:
 1. A method for balancing non-uniformly distributednetwork traffic in a wireless communications system having a pluralityof digital remote units (DRUs), the method comprising: a) providing adigital access unit (DAU) associated with a plurality of sectors of abase station; b) providing a plurality of DRUs associated with the DAU;c) partitioning the plurality of DRUs into a plurality of DRU sectors;d) measuring a number of blocked calls associated with the plurality ofDRU sectors; e) determining that the number of blocked calls is greaterthan a predetermined threshold; and f) iterating using at least elements(c) through (e).
 2. The method of claim 1 wherein iterating comprisesutilizing at least one optimization algorithm.
 3. The method of claim 2wherein the at least one optimization algorithm is at least one of aGenetic Algorithm (GA) or an Estimation Distribution Algorithm (EDA). 4.The method of claim 2 wherein the at least one optimization algorithmincludes at least one linear programming model.
 5. The method of claim 1wherein each of the DRU sectors comprises connected DRUs.
 6. The methodof claim 1 further comprising determining a total number oflicenses/sources assigned to a virtual base station (VBS).
 7. A methodfor balancing non-uniformly distributed network traffic in a DistributedAntenna System (DAS), the method comprising: a) providing a digitalaccess unit (DAU) associated with a plurality of sectors of a basestation; b) providing a plurality of Digital Remote Units (DRUs)associated with the DAU; c) partitioning the plurality of DRUs into aplurality of DRU sectors; d) measuring at least one metric for trafficconditions associated with the plurality of DRU sectors; e) comparingthe at least one metric to a predetermined threshold; f) determiningthat iteration of partitioning is warranted; and g) iterating on atleast elements (c) through (e).
 8. The method of claim 7 wherein the atleast one system metric comprises at least one of minimizing callhandoffs or maximizing a compactness index of each of the DRU sectors.9. The method of claim 7 wherein each of the plurality of DRU sectors issubstantially compact.
 10. The method of claim 7 wherein the at leastone metric comprises a maximum number of users associated with apredetermined signal to noise ratio (SNR).
 11. The method of claim 7wherein iterating comprises utilizing at least one optimizationalgorithm.
 12. The method of claim 11 wherein the at least oneoptimization algorithm is at least one of a Genetic Algorithm (GA) or anEstimation Distribution Algorithm (EDA).
 13. The method of claim 11wherein the at least one optimization algorithm includes at least onelinear programming model.
 14. The method of claim 7 wherein each of theDRU sectors comprises connected DRUs.
 15. A Distributed Antenna System(DAS) comprising: one or more Digital Access Units (DAUs); a pluralityof Digital Remote Units (DRUs) coupled to the one or more DAUs; a dataprocessor coupled to the plurality of DRUs and comprising anon-transitory computer-readable storage medium comprising a pluralityof computer-readable instructions tangibly embodied on thecomputer-readable storage medium, which, when executed by the dataprocessor, provide for balancing of network traffic, the plurality ofinstructions comprising: a) instructions that cause the data processorto partition the plurality of DRUs into a plurality of DRU sectors; b)instructions that cause the data processor to measure at least onesystem metric for traffic conditions associated with the plurality ofDRU sectors; c) instructions that cause the data processor to comparethe at least one system metric to a predetermined threshold; and d)instructions that cause the data processor to determine that the atleast one system metric is greater than the predetermined threshold; ande) iterating at least elements (a) through (d).
 16. The DAS of claim 15wherein the at least one system metric comprises a number of blockedcalls.
 17. The DAS of claim 15 wherein the at least one system metriccomprises a number of call handoffs between two of the plurality of DRUsectors.
 18. The DAS of claim 15 wherein each of the DRU sectorscomprises connected DRUs.
 19. The DAS of claim 15 wherein each of theplurality of DRU sectors is substantially compact.
 20. The DAS of claim15 wherein iterating comprises utilizing at least one optimizationalgorithm.